Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

Piñeiro-Fiel, Manuel; Moscoso, Alexis; Lado-Cacheiro, Lucía; Pombo-Pasín, María; Rey-Bretal, David; Gómez-Lado, Noemí; García, Carmelo Conesa; Silva‐Rodríguez, Jesús; Pubul, Virginia; Sánchez, Manuel; Ruibal, A; Aguiar, Pablo

doi:10.1007/s00330-020-07507-z

Cited by 9 publications

(9 citation statements)

References 53 publications

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“…Many studies reported models including both first and high-order features, which were compared usually with counterparts including a reduced number of features, so an improvement of the result can be caused by overfitting [ 353 , 354 ]. Several papers from our group [ 63 ] and others [ 60 , 126 , 317 ] have suggested a strong correlation between the most used textural features and conventional parameters such as MTV and SUV, which must be untangled.…”

Section: Discussionmentioning

confidence: 99%

“…Several papers from our group [63] and others [60,126,317] have suggested a strong correlation between the most used textural features and conventional parameters such as MTV and SUV, which must be untangled.…”

Section: Summary Of the Main Findingsmentioning

confidence: 99%

“…In addition to issues common to PET imaging itself, such as noise or partial volume effects [ 47 , 48 , 49 ], radiomics must deal with standardization issues related to the differences in acquisition and reconstruction parameters, post-processing techniques, tumor segmentation methods or even texture calculations [ 46 , 50 , 51 , 52 , 53 ]. The complex formulation of radiomics makes it challenging to explain pervasive findings such as the correlations displayed between different texture indices [ 54 , 55 , 56 , 57 , 58 , 59 , 60 ], as well as strong correlations between textural indices and tumor volume [ 61 , 62 , 63 ], which compromise the added value of these parameters in comparison with SUV or MTV. Furthermore, the variability in definitions and nomenclature of heterogeneity metrics themselves complicate any evaluation and comparison of published results [ 64 ].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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Section: Discussionmentioning

confidence: 99%

Section: Summary Of the Main Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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“…If deployed for analyzing clinical studies, a potential extension of our methods would be to first establish that there is a significant likelihood that a volume contains a genuine abnormality, and then to apply additional texture analysis metrics tailored to predict if it will become malignant. 17,44 An auxiliary benefit of our investigation was to establish which of the studied image metrics best corresponds to the visual impression of the phantom "hot" cylinder visibility. This will be useful for quantifying routinely acquired PET phantom results.…”

Section: Discussionmentioning

confidence: 99%

“…Image texture analysis can aid in identifying lung cancer and has been investigated as a means to distinguish between random noise in neighboring voxels versus a pattern of voxel values that are meaningfully correlated. 17 This type of data analysis does not ask merely whether voxel values in an individual isolated voxel exceed the minimally detectable activity indicated by the voxel value levels of neighboring background voxels, 18 but whether there is a discernable pattern of voxel values centered about a region. Clinicians do this in performing their visual analyses, but it has not been obvious which image texture analysis metrics may best reflect the confidence with which a clinician decides that a perceived PET scan voxel value fluctuation represents a genuine small lesion.…”

Section: Introductionmentioning

confidence: 99%

Computational approaches to detect small lesions in ¹⁸F‐FDG PET/CT scans

Nichols

DiFilippo

Palestro

2021

J Applied Clin Med Phys

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When physicians interpret 18 F-FDG PET/CT scans, they rely on their subjective visual impression of the presence of small lesions, the criteria for which may vary among readers. Our investigation used physical phantom scans to evaluate whether image texture analysis metrics reliably correspond to visual criteria used to identify lesions and accurately differentiate background regions from sub-centimeter simulated lesions. Methods: Routinely collected quality assurance test data were processed retrospectively for 65 different 18 F-FDG PET scans performed of standardized phantoms on eight different PET/CT systems. Phantoms included 8-, 12-, 16-, and 25-mm diameter cylinders embedded in a cylindrical water bath, prepared with 2.5:1 activity-to-background ratio emulating typical whole-body PET protocols. Voxel values in cylinder regions and background regions were sampled to compute several classes of image metrics. Two experienced physicists, blinded to quantified image metrics and to each other's readings, independently graded cylinder visibility on a 5-level scale (0 = definitely not visible to 4 = definitely visible). Results:The three largest cylinders were visible in 100% of cases with a mean visibility score of 3.3 ± 1.2, while the smallest 8-mm cylinder was visible in 58% of cases with a significantly lower mean visibility score of 1.5±1.1 (P < 0.0001). By ROC analysis, the polynomial-fit signal-to-noise ratio was the most accurate at discriminating 8-mm cylinders from the background, with accuracy greater than visual detection (93% ± 2% versus 76% ± 4%, P = 0.0001), and better sensitivity (94% versus 58%, P < 0.0001). Conclusion: Image texture analysis metrics are more sensitive than visual impressions for detecting sub-centimeter simulated lesions. Therefore, image texture analysis metrics are potentially clinically useful for 18 F-FDG PET/CT studies.

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Joint EANM/SNMMI guideline on radiomics in nuclear medicine

Hatt

Krizsán²,

Rahmim

et al. 2022

Eur J Nucl Med Mol Imaging

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Purpose The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. Methods In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. Conclusion Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

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Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

Cited by 9 publications

References 53 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Computational approaches to detect small lesions in ¹⁸F‐FDG PET/CT scans

Joint EANM/SNMMI guideline on radiomics in nuclear medicine

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Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

Cited by 9 publications

References 53 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Computational approaches to detect small lesions in 18F‐FDG PET/CT scans

Joint EANM/SNMMI guideline on radiomics in nuclear medicine

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Computational approaches to detect small lesions in ¹⁸F‐FDG PET/CT scans